Geo-harmonizer project report
Official project outputs
A complete list of project outputs is available here. To subscribe for the newsletter and receive monthly updates, please visit this link.
Software outputs
Published libraries, plugins and web-GIS solutions:
- eumap library;
- eumap QGIS plugin;
- Open Data Science Europe (ODSEurope) Viewer;
The python library eumap has been built to enable easier access to several spatial layers prepared for Continental Europe (Landsat and Sentinel mosaics, DTM and climate datasets, land cover and environment quality maps), as well the processing classes and functions used to produce them. The library implements efficient raster access through rasterio, multiple gapfiling approaches, spatial and spacetime overlay, training samples preparation (LUCAS points), and Ensemble Machine Learning applied to spatial predictions (fully compatible with scikit-learn).
All Geo-harmonized project software can be followed via the project GitLab repository.
Datasets
Datasets are usually released using Zenodo.org, in the case they exceed the limits of zenodo, they are published only via our Wasabi cloud service.
Metadata for all released layers: https://data.opendatascience.eu/geonetwork
Production steps: https://gitlab.com/geoharmonizer_inea/spatial-layers
Geo-harmonized data via the official portal for European data: https://data.europa.eu/data/datasets?catalog=opendatascienceeurope
Datasets released so far:
- Heisig, Johannes, & Hengl, Tomislav. (2020). Harmonized Tree Species Occurrence Points for Europe (0.2) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.5524611
- Hengl, T., & Parente, L. (2021). Continental Europe Digital Terrain Model geomorphometry derivatives at 30 m, 100 m and 250 m (v0.2) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.4495449
- Hengl, T., & Parente, L. (2021). MODIS LST monthly daytime and nighttime low (0.05), median (0.50) and high (0.95) temperatures for year 20** at 1-km (v1.1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.4501970
- Hengl, T. (2021). Continental Europe surface lithology based on EGDI / OneGeology map at 1:1M scale (v1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.4787632
- Hengl, Tomislav, Leal Parente, Leandro, Krizan, Josip, & Bonannella, Carmelo. (2020). Continental Europe Digital Terrain Model at 30 m resolution based on GEDI, ICESat-2, AW3D, GLO-30, EUDEM, MERIT DEM and background layers (v0.3) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.4724549
- Križan, Josip, & Antonić, Luka. (2021). Seamless 30 meter Sentinel-2 L2A Pan-European seasonal cloudless mosaics from winter 2018 to spring 2020 (1.0.1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.5155680;
- Leandro Parente, Tomislav Hengl, Josip Krizan, Martin Landa, Lukas Brodsky, & Martijn Witjes. (2020). Input dataset for gap filling and land-cover mapping using eumap Library – 2000 to 2020 (v0.3) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.4311598
- Martin Landa, Lukas Brodsky, Leandro Parente, Martijn Witjes, & Tomislav Hengl. (2021). Multi-year harmonized land cover samples based on LUCAS and CORINE datasets (v0.1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.4740691
- Ibrahim Saleem, Landa Martin, Halounová Lena, Pešek Ondřej, Pavelka Karel. (2021). GHADA (v1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.5675427
Training materials
Python tutorials:
R tutorials:
- Hengl, T., Parente, L., & Bonannella, C. (2021, September 17). Predictive mapping using spatiotemporal Ensemble ML (R tutorial). Zenodo. https://doi.org/10.5281/zenodo.5513827
Video tutorials:
- Antonić L.: “Introduction to spatial and spatiotemporal data in Python”
- Brodský L., Landa M., Bouček T.: “Working with harmonized LUCAS datasets”
- Hengl, T., Bonannella C.: “Spatiotemporal Ensemble ML in R”
- Hengl T.: “Computing with Cloud-Optimized GeoTIFFs”
- Hengl T.: “Introduction to spatial and spatiotemporal data in R”
- Hengl T.: “Modeling with spatial and spatiotemporal data in R: spatial interpolation”
- Landa M.: “Using OGC Web Services in Python”
- Parente L.: “Introduction to ODSE datasets in Python”
- Parente L., Antonić L.: “Working with Cloud-Optimized GeoTIFFs in Python”
- Parente L., van Diemen C.: “Spatiotemporal machine learning in Python”
- Parente L. “High performance computing in Python”
A copy of Videos with DOI is available via: Open Data Science Workshop series (see also complete list of videos).
Publications
To subscribe for Geo-harmonizer publications please use the project code “2018-EU-IA-0095”. Currently submitted / published publications:
- Ibrahim, S., Landa, M., Pešek, O., Pavelka, K., & Halounova, L. (2021). Space-Time Machine Learning Models to Analyze COVID-19 Pandemic Lockdown Effects on Aerosol Optical Depth over Europe. Remote Sensing, 13(15), 3027. https://doi.org/10.3390/rs13153027
- Witjes, M., Parente, L., van Diemen, C. J., Hengl, T., Landa, M., Brodsky, L., … & Glusica, L. (2021?). A spatiotemporal ensemble machine learning framework for generating land use/land cover time-series maps for Europe (2000–2019) based on LUCAS, CORINE and GLAD Landsat. PeerJ, in review, https://doi.org/10.21203/rs.3.rs-561383/v1
- Sheykhmousa, M., Mahdianpari, M., Ghanbari, H., Mohammadimanesh, F., Ghamisi, P., & Homayouni, S. (2020). Support vector machine vs. random forest for remote sensing image classification: A meta-analysis and systematic review. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. https://doi.org/10.1109/JSTARS.2020.3026724
- Ibrahim, S., & Halounova, L. (2019). Statistical study of MODIS algorithms in estimating aerosol optical depth over the Czech Republic. Civil Engineering Journal, (4). https://dx.doi.org/10.14311/CEJ.2019.04.0043
Events organized
Complete list of events is available here.
International workshops:
- Open Data Science Europe Workshop 2021, 6-10 Sept, 2021, Wageningen International Conference Center;
- Open Data Science Europe Workshop 2022, 13-16 Jun, 2022, CVUT Prague, Czech Republic;
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